from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2021-12-21 14:04:29.439735
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 21, Dec, 2021
Time: 14:04:34
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -47.5556
Nobs: 512.000 HQIC: -48.0086
Log likelihood: 5916.49 FPE: 1.05520e-21
AIC: -48.3006 Det(Omega_mle): 8.86602e-22
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.356859 0.078286 4.558 0.000
L1.Burgenland 0.099759 0.043706 2.282 0.022
L1.Kärnten -0.115564 0.022555 -5.124 0.000
L1.Niederösterreich 0.179905 0.090783 1.982 0.048
L1.Oberösterreich 0.121202 0.091598 1.323 0.186
L1.Salzburg 0.282734 0.047111 6.001 0.000
L1.Steiermark 0.021425 0.060779 0.353 0.724
L1.Tirol 0.109591 0.049059 2.234 0.025
L1.Vorarlberg -0.081100 0.043291 -1.873 0.061
L1.Wien 0.031158 0.082662 0.377 0.706
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.014154 0.172767 0.082 0.935
L1.Burgenland -0.048273 0.096455 -0.500 0.617
L1.Kärnten 0.035387 0.049777 0.711 0.477
L1.Niederösterreich -0.207473 0.200347 -1.036 0.300
L1.Oberösterreich 0.457379 0.202147 2.263 0.024
L1.Salzburg 0.313908 0.103968 3.019 0.003
L1.Steiermark 0.107518 0.134132 0.802 0.423
L1.Tirol 0.315906 0.108268 2.918 0.004
L1.Vorarlberg 0.010930 0.095538 0.114 0.909
L1.Wien 0.010550 0.182426 0.058 0.954
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.219383 0.039875 5.502 0.000
L1.Burgenland 0.093010 0.022262 4.178 0.000
L1.Kärnten -0.005320 0.011489 -0.463 0.643
L1.Niederösterreich 0.225978 0.046241 4.887 0.000
L1.Oberösterreich 0.163440 0.046656 3.503 0.000
L1.Salzburg 0.038073 0.023996 1.587 0.113
L1.Steiermark 0.029591 0.030958 0.956 0.339
L1.Tirol 0.078417 0.024989 3.138 0.002
L1.Vorarlberg 0.055673 0.022051 2.525 0.012
L1.Wien 0.102854 0.042105 2.443 0.015
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.159327 0.039273 4.057 0.000
L1.Burgenland 0.043924 0.021926 2.003 0.045
L1.Kärnten -0.013265 0.011315 -1.172 0.241
L1.Niederösterreich 0.155610 0.045542 3.417 0.001
L1.Oberösterreich 0.338981 0.045951 7.377 0.000
L1.Salzburg 0.101068 0.023634 4.276 0.000
L1.Steiermark 0.111929 0.030490 3.671 0.000
L1.Tirol 0.090089 0.024611 3.660 0.000
L1.Vorarlberg 0.054444 0.021717 2.507 0.012
L1.Wien -0.042898 0.041468 -1.034 0.301
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.159313 0.075243 2.117 0.034
L1.Burgenland -0.028084 0.042008 -0.669 0.504
L1.Kärnten -0.037310 0.021679 -1.721 0.085
L1.Niederösterreich 0.124468 0.087255 1.426 0.154
L1.Oberösterreich 0.166217 0.088038 1.888 0.059
L1.Salzburg 0.255572 0.045280 5.644 0.000
L1.Steiermark 0.086330 0.058417 1.478 0.139
L1.Tirol 0.137364 0.047153 2.913 0.004
L1.Vorarlberg 0.103176 0.041608 2.480 0.013
L1.Wien 0.035930 0.079450 0.452 0.651
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.078227 0.059027 1.325 0.185
L1.Burgenland 0.016793 0.032954 0.510 0.610
L1.Kärnten 0.050838 0.017007 2.989 0.003
L1.Niederösterreich 0.182532 0.068450 2.667 0.008
L1.Oberösterreich 0.332111 0.069064 4.809 0.000
L1.Salzburg 0.051479 0.035521 1.449 0.147
L1.Steiermark -0.004208 0.045827 -0.092 0.927
L1.Tirol 0.126451 0.036990 3.418 0.001
L1.Vorarlberg 0.059714 0.032641 1.829 0.067
L1.Wien 0.107436 0.062327 1.724 0.085
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.172555 0.071560 2.411 0.016
L1.Burgenland 0.009312 0.039952 0.233 0.816
L1.Kärnten -0.060989 0.020618 -2.958 0.003
L1.Niederösterreich -0.110144 0.082984 -1.327 0.184
L1.Oberösterreich 0.233455 0.083729 2.788 0.005
L1.Salzburg 0.039388 0.043064 0.915 0.360
L1.Steiermark 0.261595 0.055558 4.709 0.000
L1.Tirol 0.489045 0.044845 10.905 0.000
L1.Vorarlberg 0.070244 0.039572 1.775 0.076
L1.Wien -0.101505 0.075561 -1.343 0.179
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.143638 0.079236 1.813 0.070
L1.Burgenland -0.010785 0.044237 -0.244 0.807
L1.Kärnten 0.062890 0.022829 2.755 0.006
L1.Niederösterreich 0.174394 0.091884 1.898 0.058
L1.Oberösterreich -0.085305 0.092710 -0.920 0.358
L1.Salzburg 0.224501 0.047682 4.708 0.000
L1.Steiermark 0.138316 0.061517 2.248 0.025
L1.Tirol 0.054865 0.049655 1.105 0.269
L1.Vorarlberg 0.140588 0.043816 3.209 0.001
L1.Wien 0.160517 0.083665 1.919 0.055
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.459080 0.044069 10.417 0.000
L1.Burgenland 0.000263 0.024603 0.011 0.991
L1.Kärnten -0.014372 0.012697 -1.132 0.258
L1.Niederösterreich 0.181973 0.051104 3.561 0.000
L1.Oberösterreich 0.255590 0.051563 4.957 0.000
L1.Salzburg 0.019590 0.026520 0.739 0.460
L1.Steiermark -0.008806 0.034214 -0.257 0.797
L1.Tirol 0.074501 0.027617 2.698 0.007
L1.Vorarlberg 0.056892 0.024370 2.335 0.020
L1.Wien -0.023210 0.046533 -0.499 0.618
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.029724 0.091737 0.153012 0.141732 0.069019 0.081361 0.013420 0.208503
Kärnten 0.029724 1.000000 -0.031295 0.135077 0.051215 0.076003 0.454659 -0.078418 0.101439
Niederösterreich 0.091737 -0.031295 1.000000 0.288665 0.103936 0.255694 0.049623 0.147427 0.254227
Oberösterreich 0.153012 0.135077 0.288665 1.000000 0.196132 0.285940 0.155787 0.130857 0.198587
Salzburg 0.141732 0.051215 0.103936 0.196132 1.000000 0.121102 0.057079 0.111444 0.070170
Steiermark 0.069019 0.076003 0.255694 0.285940 0.121102 1.000000 0.132628 0.090210 0.012369
Tirol 0.081361 0.454659 0.049623 0.155787 0.057079 0.132628 1.000000 0.063365 0.125922
Vorarlberg 0.013420 -0.078418 0.147427 0.130857 0.111444 0.090210 0.063365 1.000000 -0.004650
Wien 0.208503 0.101439 0.254227 0.198587 0.070170 0.012369 0.125922 -0.004650 1.000000